This invention relates to recognition by machines of patterns in images.
The mechanisms by which patterns representing objects are recognized by animals has been studied extensively. A summary of studies of the human visual system is given in D. H. Hubel, "Eye, Brain, and Vision," New York, N.Y.: W. H. Freeman and Company, 1988. Machine based visual recognition schemes typically use combinations of opto-electronic devices and computer data processing techniques to recognize objects.
In general, recognizing an object requires determining Whether a certain pattern (corresponding to the object) appears within a field-of-view (FOV) of an input image. The pattern generally is defined by spatial gradients and discontinuities in luminance across the input image. Other types of gradients and discontinuities may also produce perceivable patterns. Perceivable patterns may occur in the presence of: statistical differences in textural qualities (such as orientation, shape, density, or color), binocular matching of elements of differing disparities, accretion and deletion of textural elements in moving displays, and classical `subjective contours`. An input image is here meant to include any two-dimensional, spatially ordered array of signal intensities. The signals may be of any frequency within the entire electromagnetic spectrum, such as infrared radiation signals and radar ranging signals. Thus visual recognition here denotes recognition of an object based on electromagnetic radiation received from the object.
Humans easily recognize spatial gray-scale object patterns regardless of the patterns' location or rotational orientation within a FOV. In perceiving these patterns, the human visual recognition system operates in two stages, first locating patterns of interest within the FOV, and then classifying the patterns according to known categories of objects.
Biological vision systems can rapidly segment an input image in a manner described as "preattentive." It has been found experimentally that segmentation is context-sensitive, i.e., what is perceived as a pattern at a given location can depend on patterns at nearby locations.
Contemporary image-processing techniques based on artificial intelligence (AI) systems use geometric concepts such as surface normal, curvature, and the Laplacian. These approaches were originally developed to analyze the local properties of physical processes.